Why "The Best" Doesn't Matter Anymore

For a long time, people asked the same kind of questions.

Who is the best personal injury firm?
Who is the top roofing company?
Who is the #1 plumber in town?

Those questions made sense in a world of rankings, lists, and search results. When information was scarce, averages were useful. "Best" felt like a shortcut.

But that's not how people actually make decisions anymore.

And it's definitely not how AI does.

The Question People Actually Care About

Today, nobody really wants "the best" in the abstract.

They want the right fit.

They want to know:

• Who has handled cases like mine recently?
• Who fixed roofs in my neighborhood last year?
• Who works with homes my age, my size, my materials?
• Who people around me actually used and trusted?

That's a very different question.

It's personal.
It's situational.
And it's driven by context.

AI understands this instinctively.

Why "Best" Is the Wrong Mental Model

"Best" assumes there's a single, universal answer.

But most real-world decisions don't work that way.

The best personal injury firm for a complex commercial case is not necessarily the best firm for a local car accident.
The best roofing company for new construction isn't the best choice for a 40-year-old colonial with recurring leak issues.

Rankings flatten all of that nuance.

AI doesn't.

When an AI model is asked for a recommendation, it isn't searching for the highest-rated option. It's trying to answer a more practical question:

Which business has the most relevant experience for this specific situation?

That's a fundamentally different lens.

How AI Thinks About Relevance

AI doesn't think in terms of "best overall."

It thinks in terms of:

• Similar problems solved recently
• Similar customers served successfully
• Similar geography, timing, and conditions
• Evidence that outcomes weren't one-offs

In other words, context beats credentials.

A business that looks average on a national list can be the perfect answer locally.
A firm that never shows up in "top 10" rankings can still be the most confident recommendation for a specific case.

AI is very comfortable making that distinction.

Why Recency Matters So Much

One of the most overlooked pieces of context is time.

People care deeply about what happened recently:

• Who won cases this year, not five years ago
• Who repaired roofs last winter, not a decade back
• Who is actively operating in the market right now

Recency signals relevance.
It signals that the business is still doing the work.
It signals that the experience is fresh.

AI weighs this heavily, even when humans don't realize it's happening.

Why Local Context Wins

There's also a reason people ask neighbors before they ask the internet.

Local context carries trust.

Same streets.
Same weather.
Same permitting offices.
Same inspectors.
Same courts.

AI understands that a business operating successfully in a tight geographic loop often has an edge that no generic ranking can capture.

This is why "best in America" is rarely useful, but "worked on three houses on my block" is incredibly persuasive.

Rankings Can't Do This. AI Can.

Rankings are static.
Context is dynamic.

Rankings answer broad questions.
AI answers personal ones.

That's the shift.

And once you see it, it's hard to unsee.

Context Is a Data Problem, Not a Marketing Problem

Here's the part most businesses miss.

Context isn't created with better copy.
It's created with better visibility into real activity.

Things like:

• The types of jobs you do most often
• Where those jobs actually happen
• How recently similar work was completed
• Whether customers come back
• How consistent the pattern is over time

Most businesses already have this information.

It just isn't visible in a way AI can easily reason about.

The Quiet Opportunity for SMBs

This shift is actually good news for small and mid-sized businesses.

You don't need to be the biggest.
You don't need to be the loudest.
You don't need to win on generic rankings.

You need to be clearly understood in context.

Businesses that lean into this become easier to recommend, even if they look unremarkable on the surface.

And over time, that confidence compounds.

From "Best" to "Most Relevant"

The future of discovery isn't about being universally great.

It's about being obviously right for the moment, the place, and the problem.

AI doesn't look for "the best."
It looks for the best fit.

And context is how it decides.

Founder's note: I wrote this after realizing how rarely people actually want "the best" anymore. They want the firm that handled something like their situation, nearby, and recently. AI thinks the same way.

Written by Dana Lampert, Founder of TrueSignal.

Originally published December 2025 · Reviewed periodically as the AI landscape evolves